deepseek-ai/DeepSeek-V4.1-Flash-1017 warn
Loading it runs custom code from the repo. Plus 2 minor notes.
chat template: not found · view on Hugging Face ↗
Scan coverageStatic battery2026-10-27Weights battery2026-10-27Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-10-27 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-10-27129,280-token embedding scanned · 49 undertrained |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-10-27 · published from a community scan.
medium Repo ships executable Python (trust_remote_code)
The repository contains custom code files that run in-process when loaded with trust_remote_code=True. Pin the revision hash and review the code before loading.
How to fix
Review and pin the custom code; never float on `main` with trust_remote_code=True.
- Read every `.py` file in the repo before first load — this code runs in your process.
- Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
- Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.
low Padding token is the EOS token
The pad token and the (only) EOS token are the same. Fine-tuning frameworks mask pad positions out of the loss, so training on this checkpoint teaches the model to never emit EOS — the Phi-4 / Qwen 2.5 / DeepSeek R1 infinite-generation bug. Safe to serve, hazardous to fine-tune; repoint pad_token at a dedicated token first.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
low Undertrained tokens in vocabulary (non-ASCII tail)
Embedding-norm scan flagged 49 undertrained tokens (norm < 0.3× the vocabulary median), but 4 decode to plain-ASCII strings, so exposure in English-language pipelines is limited. Behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
Check every checkpoint before it ships
Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch deepseek-ai/DeepSeek-V4.1-Flash-1017
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-10-27.
| architecture | deepseek_v41 |
| parameters | 763205.3M |
| vocabulary | 129,280 tokens |
| license | mit |
| serialization | safetensors custom code |
| chat template | none |
| glitch-token surface | 49 undertrained candidates, 4 plain-ASCII |
Full measured fingerprint
| architectures | DeepseekV41ForCausalLM |
| library | transformers |
| pipeline | image-text-to-text |
| repo files | 83 |
| revision | 1bae0e108899 |
| HF snapshot | 95.9k downloads · 3.2k likes · updated 2026-10-17 · captured 2026-10-27 |
| embedding tensor | embed.weight · BF16 · 129,280×5120 |
| embedding norms | median 8.4482 · mean 8.3008 |
| lineage check | no claimed base model |
| glitch-token samples | "FullEDMFunc", "Pagklas", "kabungtor", "that" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-10-27 05:40 | 28s | 1 |
weights run 2026-10-27 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, lineage-norm-correlation |
| embedding tensor | embed.weight · BF16 · 129,280×5120 |
| glitch surface | 49 undertrained, 4 plain-ASCII |
| lineage check | not checked (no claimed base model) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/deepseek-ai/DeepSeek-V4.1-Flash-1017)